Inertial measurement systems are used to determine the position, velocity, and attitude of an object. Typically, an inertial sensor suite is composed of a triad of accelerometers that measure the non-gravitational acceleration vector of an object with respect to an inertial frame and a triad of gyroscopes that measure the angular velocity vector of an object with respect to the inertial frame. Processing the outputs of the inertial sensors through a set of strapdown navigation algorithms yields the complete kinematic state of the object. State-of-the-art commercially available inertial navigation systems can provide position accuracies on the order of one nautical mile per hour position error growth rate.
In some existing applications, it is desirable to know the position and/or velocity of objects relative to each other, rather than in an absolute sense. However, in some other applications, it may be desirable to know both the relative and absolute positions and/or velocities of objects relative to each other. For example, an application may determine the absolute position of point B using the combination of the absolute position of point A and the relative position between points A and B. In any event, the accuracy desired in many of these applications is on the order of a centimeter, rather than a nautical mile.
Two exemplary applications that require very accurate knowledge of the relative position and/or velocity of objects include radiation-emitter location determination systems and Ultra Tightly Coupled (UTC) Inertial Navigation System (INS)/Global Positioning System (GPS). These types of systems include a master inertial sensing unit in communication with at least one remote slave inertial sensing unit that is co-located with an antenna. The instantaneous relative position and relative velocity vectors between the master and slave inertial sensor units are required to satisfy the stringent accuracy requirements placed on these systems. The nominal baseline vector between the master and slave inertial sensor units is known in such systems. However, the slave inertial sensor system and master inertial sensor system are often moving relative to each other due to vibration and flexure of the vehicle, so the baseline solution is in reality only approximately known.
In an exemplary application, one of the inertial sensor systems is located on the wing of an aircraft, and the other inertial sensor system is located on the body of the aircraft. In flight, the aircraft undergoes flexure effects at one or more prominent resonant frequencies that cause the relative position, velocity and attitude vectors between the master and slave Inertial Measurement Units (IMUs) to deviate from the baseline. In the case where the IMU is located close to the wingtip of a large aircraft, the amount of sensor position offset from the baseline can be greater than one meter. Also, in this exemplary application, an antenna co-located with the IMU responds to the same large flexure motion. Consequently, unless the relative position, velocity and attitude vectors can be corrected for the flexure motion, other onboard systems that utilize the signal from the antenna may experience degraded performance.
The above-described related patent application describes an exemplary embodiment for a novel approach that can be used to determine relative motions of structural elements at centimeter-level accuracies, in order to provide suitable relative navigation solutions. In order to obtain these solutions, the relative and master navigation processing is executed in a single processor. However, such an implementation would be very difficult in an application requiring a plurality of relative navigation solutions, such as, for example, an application including a plurality of Electronic Surveillance Measure (ESM) antennas and/or GPS antennas located on a single platform.
In this regard, many of today's aircraft have numerous navigation-related sensors located at different points on the aircraft. In many applications, it is often necessary to know where each of these sensors is relative to another point on the aircraft. Current navigation solutions take the relative positions to be static values based on measurements taken on the ground. Unfortunately, the relative positions of the sensors involved continuously change while the aircraft is in flight, which corrupts the outputs of interest from these sensors. Therefore, a pressing need exists for an approach that can be used to calculate relative navigation solutions in real-time using, for example, data from inertial sensors located at the points of interest.